Semantic understanding method and apparatus, and device and storage medium

ABSTRACT

A semantic understanding method and apparatus, and a device and a storage medium are provided. The method includes: acquiring a recognition character string that matches speech information; acquiring, from an entity vocabulary library, at least one entity vocabulary respectively corresponding to each recognition character in the recognition character string; and according to a situation of each entity vocabulary hitting the recognition character string, determining a matching entity vocabulary as a semantic understanding result of the speech information. By means of the method, insofar as a completely matching entity vocabulary is not acquired, a matching entity vocabulary can still be determined according to an entity vocabulary library, and semantic information of speech is thus accurately understood; and the method also has relatively high fault tolerance for situations such as wrong words, added words, and omitted words, such that the semantic understanding accuracy of speech information is improved.

CROSS REFERENCE OF RELATED APPLICATIONS

The present application is a continuation of International ApplicationNo. PCT/CN2021/084846, filed on Apr. 1, 2021 which claims the priorityto Chinese Patent Application No. 202010356038.8, titled “SEMANTICUNDERSTANDING METHOD AND APPARATUS, DEVICE AND STORAGE MEDIUM”, filed onApr. 29, 2020, both of which are incorporated herein by reference intheir entireties.

FIELD

The embodiment of the present disclosure relates to a computerapplication technology and a natural language processing technology, inparticular to a semantic determining method and apparatus, a device anda storage medium.

BACKGROUND

With the continuous progress of science and technology, the naturallanguage processing technology has developed rapidly, which has broughtgreat convenience to daily life of the people and industrial production.

In a related semantic understanding technology, a large number of entityvocabularies are usually stored in advance through a dictionary tree.When information to be detected is acquired, a corresponding entityvocabulary is found in the dictionary tree through character stringsearch. If the corresponding entity vocabulary is found, the entityvocabulary reflects semantics of the information to be detected. If thecorresponding entity vocabulary is not found, it indicates that there isno entity in the information to be detected.

SUMMARY

A semantic determining method and apparatus, a device and a storagemedium are provided according to the present disclosure.

In a first aspect, a semantic determining method is provided accordingto an embodiment of the present disclosure. The semantic determiningmethod includes:

acquiring a recognition character string matching with speechinformation;

acquiring, for each recognition character in the recognition characterstring, at least one entity vocabulary corresponding to the recognitioncharacter in an entity vocabulary library; and

determining, based on a hit of the entity vocabulary to the recognitioncharacter string, a matched entity vocabulary as a semantic determiningresult of the speech information.

In a second aspect, a semantic determining apparatus is providedaccording to an embodiment of the present disclosure. The semanticdetermining apparatus includes: a recognition character stringacquisition module, an entity vocabulary acquisition module and asemantic determining result determination module.

The recognition character string acquisition module is configured toacquire a recognition character string matching with speech information.

The entity vocabulary acquisition module is configured to acquire, foreach recognition character in the recognition character string, at leastone entity vocabulary corresponding to the recognition character in anentity vocabulary library.

The semantic determining result determination module is configured todetermine, based on a hit of the entity vocabulary to the recognitioncharacter string, a matched entity vocabulary as a semantic determiningresult of the speech information.

In a third aspect, an electronic device is provided according to anembodiment of the present disclosure. The electronic device includes amemory, a processing device, and a computer program stored on the memoryand operable on the processing device, wherein the processing device,when executing the program, implements the semantic determining methodaccording to any embodiment of the present disclosure.

In a fourth aspect, a storage medium containing computer executableinstructions is provided according to an embodiment of the presentdisclosure. The computer executable instructions, when executed by acomputer processor, cause the computer processor to implement thesemantic determining method according to any embodiment of the presentdisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In conjunction with the drawings and with reference to the followingembodiments, the above and other features, advantages and aspects of theembodiments of the present disclosure are more apparent. The same orsimilar reference numerals throughout the drawings represent the same orsimilar elements. It should be understood that the drawings areschematic and the components and elements are unnecessarily drawn toscale.

FIG. 1 is a flowchart of a semantic determining method according to afirst embodiment of the present disclosure;

FIG. 2A is a flowchart of a semantic determining method according to asecond embodiment of the present disclosure;

FIG. 2B is a flowchart of a semantic determining method according to aspecific first application scenario of the present disclosure;

FIG. 3 is a structural block diagram of a semantic determining apparatusaccording to a third embodiment of the present disclosure; and

FIG. 4 is a structural block diagram of an electronic device accordingto a fourth embodiment of the present disclosure.

DETAILED DESCRIPTION

The embodiments of the present disclosure are described in detail belowwith reference to the drawings. Although some embodiments of the presentdisclosure are shown in the drawings, it should be understood that thepresent disclosure may be implemented in various forms and should not belimited to the embodiments. The embodiments are provided for thoroughlyand completely understanding the present disclosure. It should beunderstood that the drawings and the embodiments of the presentdisclosure are exemplary and are not intended to limit the protectionscope of the present disclosure.

It should be understood that the steps in the method embodiments of thepresent disclosure may be performed in different orders and/or inparallel. In addition, the method embodiments may include additionalsteps and/or omit to perform the illustrated steps, not limiting thescope of the present disclosure.

The term “include” and its variations in the present disclosure meansopen-ended inclusion, that is, “including but not limited to”. The term“based on” means “based at least in part on”. The term “one embodiment”means “at least one embodiment”. The term “another embodiment” means “atleast one additional embodiment”. The term “some embodiments” means “atleast some embodiments”. The definitions of other terms are provided inthe following descriptions.

It should be noted that the concepts such as “first” and “second”mentioned in the present disclosure are used to distinguish differentapparatuses, modules or units, and are not used to limit an sequentialorder or interdependence of the functions performed by the apparatuses,modules or units.

It should be noted that the modifications such as “one” and “multiple”mentioned in the present disclosure are illustrative and notrestrictive. Those skilled in the art should understand that themodifications should be understood as “one or more” unless otherwiseexpressly indicated in the context.

A name of a message or information exchanged between multipleapparatuses in the embodiments of the present disclosure are forillustrative purposes only, and are not intended to limit the scope ofthese messages or information.

FIG. 1 is a flowchart of a semantic determining method according to afirst embodiment of the present disclosure. This embodiment may beapplied to the situation of accurately understanding semantics of speechinformation based on an entity vocabulary library. The method may beperformed by a semantic determining apparatus in the embodiment of thepresent disclosure, and the semantic determining apparatus may beimplemented by software and/or hardware and integrated in a terminaldevice or a server. The method includes the following S110 to S130.

In S110, a recognition character string matching with speech informationis acquired.

Automatic Speech Recognition (ASR) technology is applied to the acquiredspeech information to acquire a matched recognition character string.Specifically, a speech recognition model may be established based on alarge amount of corpus information after speech signal processing andknowledge mining Once a target speech information is acquired, speechfeature extraction and feature matching is performed on the targetspeech information by the trained speech recognition model, to acquirethe recognition character string of the target speech information. In anembodiment of the present invention, the technical means adopted forspeech recognition are not specifically limited, and the type of thespeech recognition model is not specifically limited.

In S120, for each recognition character in the recognition characterstring, at least one entity vocabulary corresponding to the recognitioncharacter is acquired in the entity vocabulary library.

An entity vocabulary includes nouns and pronouns and other words withpractical significance. Specifically, the entity vocabulary may includenames of things with specific significance and complete sentences, suchas verses, lyrics, famous quotes. In the embodiment of the presentinvention, the type of entity vocabulary in the entity vocabularylibrary is not specifically limited. Based on each character in therecognition character string, an entity vocabulary containing one ormore characters in the recognition character string in the entityvocabulary library is acquired. For example, the recognition characterstring acquired according to the speech information is “Bright and clearlight in front of my bed”. Then, corresponding entity vocabularies areacquired based on the characters “bed”, “front”, “bright”, “clear” and“light”, and entity vocabularies “Bright light of the moon in front ofmy bed”, “The bright moon shines on the pines” and “What time does thebright moon appear” are acquired.

Optionally, in an embodiment of the present invention, before acquiringthe recognition character string matching with the speech information,the method further includes: establishing a descending index tablematching with the entity vocabulary library based on the entityvocabularies included in the entity vocabulary library. The descendingindex table includes multiple key-value pairs. A key name in thekey-value pairs is a character, and a value in the key-value pairs is atleast one entity vocabulary containing the character. The at least oneentity vocabulary corresponding to the recognition character is acquiredin the entity vocabulary library by: querying the descending index tablematching with the entity vocabulary library, and acquiring the at leastone entity vocabulary corresponding to the recognition character in therecognition character string. Each entity vocabulary in the entityvocabulary library is stored in the descending index table in the formof key-value pair. The key name is a character included in the entityvocabulary library, and the value is the entity vocabulary correspondingto the key name in the entity vocabulary library. For example, if theentity vocabulary is “Bright light of the moon in front of my bed”, inthe key-value pairs with key names “bed”, “front”, “bright”, “moon” and“light”, the “Bright light of the moon in front of my bed” is the valuecorresponding to the above key values. In addition, there are othervalues corresponding to the above characters. For example, the key-valuepair with the key name “bright” further includes values “The bright moonshines on the pines” and “what time does the bright moon appear”. Thenumber of characters in the entity vocabulary is relatively fixed (forexample, the total number of Chinese characters is relatively fixed),but the variations of the entity vocabulary formed by the combination ofcharacters are diverse and numerous. Therefore, the utilization of thedescending index table to record each entity vocabulary in the entityvocabulary library improves the retrieval efficiency of the entityvocabulary. For the update of the entity vocabulary library, forexample, the addition of an entity vocabulary, a new entity vocabularyis only required to associate with each of the corresponding characters.If the new entity vocabulary contains a new character, the new characteris taken as a new key name, and the entity vocabulary itself is alsotaken as a value corresponding to the new key name In this way, theentity vocabulary library is updated, and the efficiency of storing theentity vocabulary is improved.

In an embodiment of the present invention, after at least one entityvocabulary corresponding to the recognition character is acquired, themethod further includes: determining whether there is a duplicate entityvocabulary in the acquired entity vocabularies; and deleting theduplicate entity vocabulary to ensure no duplicate vocabulary in theentity vocabularies acquired based on each recognition character in therecognition character string in the case that there is a duplicateentity vocabulary in the acquired entity vocabularies. In this way, thenumber of entity vocabularies to be determined is reduced when the hitof each entity vocabulary on the recognition character string isdetermined, which improves the efficiency of acquiring the entityvocabulary matching with the speech information.

In an embodiment of the present invention, before the at least oneentity vocabulary corresponding to the recognition character is acquiredin the entity vocabulary library, the method further includes: acquiringeffective recognition characters in the recognition character stringbased on the recognition character string and a preset invalid characterlist; For each recognition character in the recognition characterstring, the at least one entity vocabulary corresponding to therecognition character in the recognition character string is acquired inthe entity vocabulary library, by acquiring, in the entity vocabularylibrary, at least one entity vocabulary corresponding to an effectiverecognition character in the recognition character string. Meaninglesscharacters and/or vocabularies without obvious indication are pre-storedin the preset invalid character list, such as “of”, “Huh”, “this” and“that”. After the recognition character string is acquired, therecognition character string is filtered based on invalid characters inthe preset invalid character list, to take other characters in therecognition character string except for the invalid characters as validrecognition characters. Then, the corresponding entity vocabulary isacquired based on the valid recognition characters to acquire an entityvocabulary in a targeted way, which improves acquisition efficiency.

In S130, a matched entity vocabulary is determined as a semanticdetermining result of the speech information, based on a hit of eachentity vocabulary to the recognition character string.

The matched entity vocabulary is determined, based on the hit of theentity vocabulary to the recognition character string, that is, based onthe number of the same characters between each entity vocabulary and therecognition character string. Then, the semantics of the speechinformation is accurately understood based on the entity vocabulary.Taking the above technical solution as an example, the recognitioncharacter string is “Bright and clear light in front of my bed”. Theacquired entity vocabularies are “Bright light of the moon in front ofmy bed”, “The bright moon shines on the pines” and “What time does thebright moon appear”. Thus, the number of the same characters between theabove entity vocabularies and the recognition character string is 4, 1and 1. The entity vocabulary matching with the speech information isdetermined to be “Bright light of the moon in front of my bed”. Based onthis, the accurate matching between the speech information and theentity vocabulary in entity vocabulary library is realized, which hasstrong fault-tolerance especially for the wrong word, the extra word orthe missing word.

In the above technical solution, the number of characters of the entityvocabulary and the recognition character string may be the same, and acharacter similarity may be determined based on the number of the samecharacters. In another embodiment, for the case that the number ofcharacters of the entity vocabulary is different from that of therecognition character string, the character similarity needs to bedetermined by multiple local recognition character strings in therecognition character string. Specifically, the matched entityvocabulary as the semantic determining result of the speech informationis determined based on the hit of the entity vocabulary to therecognition character string, by traversing, based on a character lengthof the entity vocabulary, local recognition character strings matchingwith the character length in the recognition character string; andcalculating local similarities between the local recognition characterstrings and the matched entity vocabulary; determining, based on thelocal similarities between the recognition character string and theentity vocabulary, the entity vocabulary matching with the speechinformation. For example, the recognition character string is ““Brightand clear light in front of my bed”'s next sentence”, and the acquiredentity vocabulary includes “Bright light of the moon in front of mybed”, “The bright moon shines on the pines” and “What time does thebright moon appear”, where the character length of “Bright light of themoon in front of my bed (

ti in Chinese)” is 5 characters. The local recognition character stringsare traversed in the recognition character string ““Bright and clearlight in front of my bed”'s next sentence”. That is, “Bright light ofthe moon in front of my bed” is compared with the local recognitioncharacter strings “Bright and clear light in front of my bed”, “clearlight in front of my bed”'s next” and “light in front of my bed”'s nextsentence” respectively to acquire the local similarities. “Bright lightof the moon in front of my bed” has the highest local similarity withthe local recognition character string “Bright and clear light in frontof my bed”, and the highest local similarity is used as the similaritybetween the entity vocabulary “Bright light of the moon in front of mybed” and the recognition character string ““Bright and clear light infront of my bed”'s next sentence”. In the same way, the similaritybetween “The bright moon shines on the pines” and the recognitioncharacter string ““Bright and clear light in front of my bed”'s nextsentence”, and the similarity between “what time does the bright moonappear” and the recognition character string ““Bright and clear light infront of my bed”'s next sentence” are acquired. Then, “Bright light ofthe moon in front of my bed” are finally determined as the matchedentity vocabulary, and the matched entity vocabulary is added to thesemantic determining result.

In particular, the semantic determining result may be unique. Thesemantic determining result is returned and showed, or a follow-upoperation is performed directly based on the entity vocabulary andcharacter information in the speech information. Taking the recognitioncharacter string ““Bright and clear light in front of my bed”'s nextsentence” in the above technical solution as an example, “Are youlooking for ‘The next sentence of Bright light of the moon in front ofmy bed’?” may be returned, after the matched entity vocabulary isdetermined as “Bright light of the moon in front of my bed”. When aconfirmation instruction is received, a follow-up operation is performedto acquire the corresponding information of the next sentence.Alternatively, after the matched entity vocabulary is determined as“Bright light of the moon in front of my bed”, information of the nextsentence matching with “Bright light of the moon in front of my bed” isdirectly found. In an embodiment, the semantic determining result may benot unique. For example, after that the matched entity vocabulary isdetermined as “The bright moon is rising above the sea” and “To invitethe moon, I raise my cup”, the above entity vocabularies are added tothe semantic determining result and the semantic determining result isreturned. When a selection instruction is acquired, a follow-upoperation is performed based on the selection instruction.

In an embodiment, if the entity vocabulary contains the more characters,it is more likely to hit the recognition character string. Therefore,the similarities between the entity vocabulary and the local recognitioncharacter strings cannot be accurately reflected only in dependence onthe number of the same characters in the entity vocabulary and the localrecognition character strings. In an embodiment of the presentinvention, the number of the same characters in the entity vocabularyand the local recognition character string divided by the number ofcharacters in the entity vocabulary is equal to the local similarity, toimprove the accuracy of the acquired local similarity.

In the technical solution of the embodiments of the present disclosure,after acquiring the recognition character string matching with thespeech information, at least one entity vocabulary corresponding to therecognition character in the recognition character string is acquiredfrom the entity vocabulary library, and the matched entity vocabulary isdetermined as the semantic determining result of the speech informationbased on the hit of the entity vocabulary to the recognition characterstring. Thus, in the case of not acquiring the exactly matched entityvocabulary, the matched entity vocabulary may still be determined basedon the entity vocabulary library, and then semantic information of aspeech may be accurately understood. At the same time, strongfault-tolerance for a wrong word, an extra word or a missing wordimproves the accuracy of semantic determining of the speech information.

FIG. 2A is a flowchart of the semantic determining method according to asecond embodiment of the present disclosure. This embodiment isspecified on the basis of the above technical solution. In theembodiment of the present invention, the character similarity betweenthe entity vocabulary and the recognition character string is acquiredthrough a sliding window. In this case, the entity vocabulary matchingwith the speech information is determined based on a preset charactersimilarity threshold condition and the character similarity between therecognition character string and the entity vocabulary. The methodincludes the following S210 to S280.

In S210, the recognition character string matching with the speechinformation is acquired.

In S220, for each recognition character in the recognition characterstring, at least one entity vocabulary corresponding to the recognitioncharacter is acquired in the entity vocabulary library.

In S230, a target character length matching with a target entityvocabulary currently processed is acquired, and a sliding windowmatching with the target character length is set.

The sliding window is a flow control technology, in which thetransmission amount of byte data is managed by the size of the setsliding window. In an embodiment of the present invention, the slidingwindow is configured to traverse the recognition character string, toensure that the number of characters in the sliding window is equal tothat of the target entity vocabulary currently processed, or that thenumber of characters in the sliding window reaches a maximum number ofcharacters in the recognition character string, Therefore, the length ofthe sliding window matches with the character length of the entityvocabulary currently processed. For example, if the entity vocabularycurrently processed is “Bright light of the moon in front of my bed (

in Chinese)”, the length of the corresponding sliding window is 5characters. If the entity vocabulary currently processed is “Hard is theway to Shu (

in Chinese)”, the length of the corresponding sliding window is 3characters. In particular, the sliding window may be controlled in aparallel way. That is, each acquired entity vocabulary may be assigned acorresponding sliding window. Respective entity vocabularies may betraversed simultaneously through the corresponding sliding windows.Alternatively, the sliding window may be controlled in a serial way.That is, the recognition character string is traversed through onesliding window, and compared with each of the entity vocabularies inturn. In the embodiment of the present invention, the control mode ofthe sliding window is not specifically limited.

In S240, a target local recognition character string matching with thesliding window is acquired based on a position of a sliding startingpoint of the sliding window in the recognition character string, wherean initial position of the sliding starting point is a first characterof the recognition character string.

The recognition character string is traversed through the sliding windowfrom left to right. The sliding starting point of the sliding window islocated at a position of the left end point of the sliding window in therecognition character string. For example, the length of the slidingwindow is 5 characters, and the current left end point is located at theposition of a third character of the recognition character string““Bright and clear light in front of my bed”'s next sentence” (

in Chinese). In this case, the position of the third character of therecognition character string is the sliding starting point, and thecorresponding target local recognition character string is “clear lightin front of my bed”'s next” (

in Chinese). The initial position of the sliding window is the positionof the first character, and the corresponding target local recognitioncharacter string is “Bright and clear light in front of my bed”.

In S250, the similarity between the target entity vocabulary and thetarget local recognition character string is calculated as a localsimilarity.

Taking the above technical solution as an example, the similaritybetween the entity vocabulary “Bright light of the moon in front of mybed” and the local recognition character string “Bright and clear lightin front of my bed” is calculated. The number of the same charactersbetween the entity vocabulary “Bright light of the moon in front of mybed” and the local recognition character string “Bright and clear lightin front of my bed” is 4, and the entity vocabulary “Bright light of themoon in front of my bed” includes 5 characters, and thus the localsimilarity acquired is 4/5, that is, 80%.

In S260, the position of the sliding starting point is updated to aposition of the next character, an operation of acquiring the targetlocal recognition character string matching with the sliding windowbased on the position of the sliding starting point of the slidingwindow in the recognition character string is performed repeatedly,until the target local recognition character string includes the lastcharacter of the recognition character string.

Taking the above technical solution as an example, the position of thesliding starting point is updated to the position of the next character.That is, the local recognition character string “clear light in front ofmy bed”'s” (

in Chinese) is obtained, and the corresponding local similarity is 3/5,that is, 60%. Then, the local recognition character string “in front ofmy bed”'s next” (

in Chinese) is acquired, the corresponding local similarity is 2/5, thatis, 40%, until the local recognition character string “of my bed”'s nextsentence” (

in Chinese) is acquired, the corresponding local similarity is 1/5, thatis, 20%. At this time, the target local recognition character string hasincluded the last character of the recognition character string. In suchcase, all local recognition character strings matching with the entityvocabulary in the recognition character string have been acquired.

In S270, in the local similarities between the recognition characterstring and each of the entity vocabularies, the maximum local similarityis selected as the character similarity between the recognitioncharacter string and each of the entity vocabularies.

Taking the above technical solution as an example, the local similaritybetween the entity vocabulary “Bright light of the moon in front of mybed” and the recognition character string is 80%, 60%, 40% and 20%respectively. The maximum local similarity, that is, 80%, is selected asthe character similarity between the recognition character string andthe entity vocabulary “Bright light of the moon in front of my bed”. Inthe same way, the local similarity between the entity vocabulary “Thebright moon shines on the pines” and the recognition character string is20%, 20%, 20% and 0% respectively. The maximum local similarity, thatis, 20%, is selected as the character similarity between the recognitioncharacter string and the entity vocabulary “The bright moon shines onthe pines”. The local similarity between the entity vocabulary “Whattime does the bright moon appear” and the recognition character stringis 20%, 20%, 20% and 0% respectively, and the maximum local similarity,that is, 20% is selected as the character similarity between therecognition character string and the entity vocabulary “What time doesthe bright moon appear”.

In S280, the entity vocabulary matching with the speech information isdetermined based on the character similarities and a preset charactersimilarity threshold condition.

The matched relationship between the recognition character string andthe entity vocabulary is not only affected by the character similarity,but also related to the character length of the entity vocabulary.Therefore, the preset character similarity threshold conditions includea corresponding relationship between the character length of the entityvocabulary and a character similarity threshold. Different characterlengths of the entity vocabulary may correspond to different charactersimilarity thresholds. For example, for the entity vocabulary with acharacter length of less than or equal to 3 characters, the charactersimilarity threshold condition is set to 100%. That is, only when theentity vocabulary completely corresponds to the local recognitioncharacter string in the recognition character string, the entityvocabulary can be used as the semantic determining result of the speechinformation. For the entity vocabulary with a character length of morethan or equal to 4 characters, the character similarity thresholdcondition is set to 80%. That is, when the similarity between the entityvocabulary and the local recognition character string in the recognitioncharacter string reaches more than 80%, the entity vocabulary may beused as the semantic determining result of the speech information.

In an embodiment of the present invention, after the similarity betweenthe target entity vocabulary and the target local recognition characterstring is calculated as the local similarity, the method furtherincludes: recording the position of the sliding starting point and thesize of the sliding window. The matched entity vocabulary is determinedas the semantic determining result of the speech information furtherbased on the hit of the entity vocabulary to the recognition characterstring, by acquiring the position of the sliding starting point and thesize of the sliding window corresponding to the target charactersimilarity based on the target character similarity corresponding to thematched entity vocabulary; adding the acquired position of the slidingstarting point and the acquired size of the sliding window to thesemantic determining result as remark information. After the matchedentity vocabulary is acquired, the position of the sliding startingpoint and the size of the sliding window corresponding to the targetcharacter similarity may be added to the semantic determining result todescribe detailed information of the entity vocabulary. In this way, thereturned semantic determining result includes not only the acquiredentity vocabulary, but also the corresponding relationship between theentity vocabulary and the recognition character string. For example,taking the above technical solution as an example, the sliding startingpoint corresponding to the finally matched entity vocabulary “Brightlight of the moon in front of my bed” is the first character, and asliding end point is a fifth character (that is, “Bright light of themoon in front of my bed” corresponds to “Bright and clear light in frontof my bed” in ““Bright and clear light in front of my bed”'s nextsentence”).

In the technical solution of the embodiment of the present disclosure,after the recognition character string matching with the speechinformation is acquired, for each recognition character in therecognition character string, at least one entity vocabularycorresponding to the recognition character is acquired from the entityvocabulary library, and the similarity between the entity vocabulary andthe recognition character string is acquired through the sliding window.In this way, in the case of not acquiring the exactly matched entityvocabulary, the matched entity vocabulary may be determined based on theentity vocabulary library, and then the semantic information of thespeech may be accurately understood. In addition, the entity vocabularythat matches with the speech information is determined, based on thecharacter similarity between the recognition character string and theentity vocabulary, as well as the preset character similarity thresholdcondition, which avoids the influence of the character length of theentity vocabulary on the matching result and improves the accuracy ofsemantic determining of the speech information.

First Specific Application Scenario

FIG. 2B is a flowchart of a semantic determining method according to afirst specific application scenario of the present disclosure. Themethod includes the following S301 to S305.

In S310, a verse entity vocabulary library is acquired.

The verse entity vocabulary library includes “Took it to be frost on theground”, “Raised my head to gaze at the moon”, “And lowered it to thinkof home”, “Tossing and turning on the bed” and other many verse entityvocabularies.

In S320, a descending index table is established based on the verseentity vocabularies in the verse entity vocabulary library, where thedescending index table includes multiple key-value pairs, the key namein the key-value pair is a character, and the value in the key-valuepair is at least one verse entity vocabulary containing the character.

For example, the values corresponding to the key name “bed” include“Bright light of the moon in front of my bed” and “Leakage of rain in myroom, no dry place in the head of the bed”. The values corresponding tothe key name “moon” include “A bird in the mountain startled by themoon” and “Bright light of the moon in front of my bed”. The valuescorresponding to the key name “front” include “Eyesight covered by ajade in front of the crown” and “Bright light of the moon in front of mybed”. The values corresponding to the key name “who” include “Who knowsthe rice that feeds” and “Who may bear to watch dusts on the road”.

In S330, a query statement matching with the speech information isacquired.

For example, the matched query statement acquired through the speechrecognition technology is ““bright and clear light in front of my bed”'snext sentence”.

In S340, The character similarity between the query statement and eachof the verse entity vocabularies is determined through the slidingwindow.

Specifically, the target character length that matches with the targetentity vocabulary currently processed is acquired, and a sliding windowmatching with the target character length is set. Based on the positionof the sliding starting point of the sliding window in the querystatement, a target local query statement matching with the slidingwindow is acquired. The initial position of the sliding starting pointis the first character of the query statement. The similarity betweenthe target entity vocabulary and the target local query statement iscalculated as a local similarity. The position of the sliding startingpoint is updated to a position of the next character. The operation ofacquiring the target local query statement matching with the slidingwindow based on the position of the sliding starting point of thesliding window in the query statement is performed repeatedly, until thetarget local query statement includes a last character of the querystatement. From the local similarities between the query statement andeach of the entity vocabularies, the maximum local similarity isselected as the character similarity between the query statement andeach of the entity vocabularies. For example, it is determined that thecharacter similarity between the query statement and “Bright light ofthe moon in front of my bed” is 80%.

In S350, The verse entity vocabulary matching with the query statementis determined as a semantic determining result of the query statement,and the sliding start position and sliding end position of the slidingwindow is recorded.

For example, based on the character similarity, the verse entityvocabulary that matches with the query statement is determined as“Bright light of the moon in front of my bed”. The sliding startingpoint position is recorded as a first character, and the sliding endposition is recorded as a fifth character.

In the technical solution of the embodiment of the present disclosure, adescending index table is established based on the verse entityvocabularies in the verse entity vocabulary library. After the querystatement is acquired, the character similarity between the querystatement and each of the verse entity vocabularies is determinedthrough the sliding window. Then, the entity vocabulary matching withthe query statement is determined as the semantic determining result ofthe query statement, and the sliding start position and sliding endposition of the sliding window are recorded. In this way, in the case ofnot acquiring the exactly matched entity vocabulary, the matched verseentity vocabulary may be determined based on the verse entity vocabularylibrary, and then the semantic information of the speech is accuratelyunderstood. In addition, strong fault-tolerance for a wrong word, anextra word or a missing word improves the accuracy of semanticdetermining of the speech information.

FIG. 3 is a structural block diagram of a semantic determining apparatusaccording to a third embodiment of the present disclosure. The semanticdetermining apparatus includes a recognition character stringacquisition module 310, an entity vocabulary acquisition module 320 anda semantic determining result determination module 330.

The recognition character string acquisition module 310 is configured toacquire a recognition character string matching with speech information.

The entity vocabulary acquisition module 320 is configured to acquire,for each recognition character in the recognition character string, atleast one entity vocabulary corresponding to the recognition character,in the entity vocabulary library.

The semantic determining result determination module 330 is configuredto determine a matched entity vocabulary as a semantic determiningresult of the speech information, based on a hit of the entityvocabulary to the recognition character string.

In the technical solution of the embodiment of the present disclosure,after the recognition character string matching with the speechinformation is acquired, for each recognition character in therecognition character string, at least one entity vocabularycorresponding to the recognition character in the recognition characterstring is acquired from the entity vocabulary library, and the matchedentity vocabulary is determined as the semantic determining result ofthe speech information based on the hit of the entity vocabulary to therecognition character string. Thus, in the case of not acquiring theexactly matched entity vocabulary, the matched entity vocabulary may bedetermined based on the entity vocabulary library, and then the semanticinformation of the speech may be accurately understood. In addition,strong fault-tolerance for a wrong word, an extra word or a missing wordimproves the accuracy of semantic determining of the speech information.

Optionally, on the basis of the above technical solution, the semanticdetermining apparatus further includes: a descending index tableestablishment module configured to establish a descending index tablematching with the entity vocabulary library, based on entityvocabularies in the entity vocabulary library, wherein the descendingindex table includes multiple key-value pairs, each of the key-valuepairs includes a key name which is a character, and a value which is atleast one entity vocabulary containing the character.

Optionally, on the basis of the above technical solution, the entityvocabulary acquisition module 320 is configured to query the descendingindex table matching with the entity vocabulary library, and acquire theat least one entity vocabulary corresponding to the recognitioncharacter in the recognition character string.

Optionally, on the basis of the above technical solution, the semanticdetermining result determination module 330 includes: a local similarityacquisition unit and an entity vocabulary determination unit.

The local similarity acquisition unit is configured to traverse, basedon a character length of the entity vocabulary, local recognitioncharacter strings matching with the character length in the recognitioncharacter string; and calculate a local similarity between each of thelocal recognition character strings and the matched entity vocabulary.

The entity vocabulary determination unit is configured to determine theentity vocabulary matching with the speech information, based on thelocal similarities between the recognition character string and each ofthe entity vocabularies.

Optionally, on the basis of the above technical solution, the localsimilarity acquisition unit includes: a sliding window setting sub-unit,a local recognition character string acquisition sub-unit, a similaritycalculation sub-unit, and a sliding starting point position updatesub-unit.

The sliding window setting sub-unit is configured to acquire a targetcharacter length matching with a target entity vocabulary currentlyprocessed, and set a sliding window matching with the target characterlength.

The local recognition character string acquisition sub-unit isconfigured to acquire a target local recognition character stringmatching with the sliding window, based on a position of a slidingstarting point of the sliding window in the recognition characterstring, where an initial position of the sliding starting point is afirst character of the recognition character string.

The similarity calculation sub-unit is configured to calculate asimilarity between the target entity vocabulary and the target localrecognition character string as the local similarity.

The sliding starting point position update sub-unit is configured toupdate the position of the sliding starting point to a position of anext character; and return to an operation of acquiring the target localrecognition character string matching with the sliding window based onthe position of the sliding starting point of the sliding window of inthe recognition character string, until the target local recognitioncharacter string includes a last character of the recognition characterstring.

Optionally, on the basis of the above technical solution, the entityvocabulary determination unit includes: a maximum local similarityacquisition sub-unit and an entity vocabulary determination sub-unit.

The maximum local similarity acquisition sub-unit is configured toselect a maximum local similarity from the local similarities betweenthe recognition character string and each of the entity vocabularies, asa character similarity between the recognition character string and theentity vocabulary;

The entity vocabulary determination sub-unit is configured to determinethe entity vocabulary matching with the speech information, based on thecharacter similarity and a preset character similarity thresholdcondition.

Optionally, on the basis of the above technical solution, the semanticdetermining apparatus further includes: a recording execution moduleconfigured to record the position of the sliding starting point and asize of the sliding window.

Optionally, on the basis of the above technical solution, the semanticdetermining result determination module 330 further includes: a positionand window size acquisition unit and a position and window sizedetermination unit.

The position and window size acquisition unit is configured to acquirethe position of the sliding starting point and the size of the slidingwindow corresponding to a target character similarity, based on thetarget character similarity corresponding to the matched entityvocabulary.

The position and window size determination unit is configured to add theacquired position of the sliding starting point and the acquired size ofthe sliding window to the semantic determining result as remarkinformation.

Optionally, on the basis of the above technical solution, the semanticdetermining apparatus further includes: an effective recognitioncharacter acquisition module configured to acquire effective recognitioncharacters in the recognition character string, based on the recognitioncharacter string and a preset invalid character list.

Optionally, on the basis of the above technical solution, the entityvocabulary acquisition module 320 is further configured to acquire, foreach of the effective recognition characters in the recognitioncharacter string, at least one entity vocabulary corresponding to theeffective recognition character in the entity vocabulary library.

The above apparatus may perform the semantic determining methodaccording to any embodiment of the present disclosure, and hascorresponding functional modules and beneficial effects to perform themethod. Technical details not described in detail in this embodiment canrefer to the method according to any embodiment of this disclosure.

FIG. 4 shows a schematic structural diagram of an electronic device(such as the terminal device or the server in FIG. 1 ) 400 suitable forimplementing an embodiment of the present disclosure. The terminaldevice in the embodiment of the present disclosure may include, but arenot limited to, mobile phones, laptops, digital broadcast receivers,PDAs (personal digital assistants), PADs (tablets), PMPs (portablemultimedia players), vehicle-mounted terminals (such as in-vehiclenavigation terminals), and other mobile phone terminals and fixedterminals such as digital TVs, desktop computers, and the like. Theelectronic device shown in FIG. 4 is only exemplary, and should notindicate any limitation to the functions and scope of application of theembodiments of the present disclosure.

As shown in FIG. 4 , the electronic device 400 may include a processingapparatus 401, such as a central processor (CPU) or a graphicsprocessor, which may execute various operations and processing based ona program stored in a Read Only Memory (ROM) 402 or a program loadedfrom a storage apparatus 408 into a Random Access Memory (RAM) 403. TheRAM 403 is further configured to store various programs and datarequired by the electronic device 400. The processing apparatus 401, theROM 402 and the RAM 403 are connected to each other through a bus 404.An Input/output (I/O) interface 405 is also connected to the bus 404.

Generally, the I/O interface 405 may be connected to: an input apparatus406, such as a touch screen, a touch panel, a keyboard, a mouse, acamera, a microphone, an accelerometer, and a gyroscope; an outputapparatus 407, such as a liquid crystal display (LCD), a speaker, and avibrator; a storage apparatus 408 such as a magnetic tape and a harddisk; and a communication apparatus 409. The communication apparatus 409enables wireless or wired communication between the electronic device400 and other devices for data exchanging. Although FIG. 4 shows anelectronic device 400 having various components, it should be understoodthat the illustrated components are not necessarily required to all beimplemented or embodied. Alternatively, more or fewer devices may beimplemented or included.

Particularly, according to an embodiment of the present disclosure, theprocess described above in conjunction with flow charts may beimplemented as a computer program. For example, a computer programproduct is further provided as an embodiment in the present disclosure,including a computer program carried on a non-transient computerreadable medium. The computer program includes program code forperforming the method shown in the flowchart. In the embodiment, thecomputer program may be downloaded and installed from the network viathe communication apparatus 409, or installed from the storage 406, orinstalled from the ROM 402. When the computer program is executed by theprocessing apparatus 401, the functions defined in the method accordingto the embodiment of the present disclosure are performed.

It is to be noted that, the computer readable medium mentioned hereinmay be a computer readable signal medium or a computer readable storagemedium or any combination thereof. The computer readable storage mediummay be but is not limited to, a system, an apparatus, or a device in anelectronic, magnetic, optical, electromagnetic, infrared, orsemi-conductive form, or any combination thereof. The computer readablestorage medium may be, but is not limited to, an electrical connectionwith one or more wires, a portable computer disk, a hard disk, a randomaccess memory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or flash memory), an optical fiber, a portablecompact disc read-only memory (CD-ROM), a light storage device, amagnetic storage apparatus or any proper combination thereof. In theembodiment of the present disclosure, the computer readable storagemedium may be any tangible medium containing or storing a program, andthe program may be used by or in combination with an instructionexecution system, apparatus, or device. In the embodiment of the presentdisclosure, the computer readable signal medium may be a data signaltransmitted in a baseband or transmitted as a part of a carrier wave andcarrying computer readable program codes. The transmitted data signalmay be in various forms, including but not limited to an electromagneticsignal, an optical signal or any proper combination thereof. Thecomputer readable signal medium may be any computer readable mediumother than the computer readable storage medium, and can send, propagateor transmit programs to be used by or in combination with an instructionexecution system, apparatus or device. The program codes stored in thecomputer readable medium may be transmitted via any proper mediumincluding but not limited to: a wire, an optical cable, radio frequencyand the like, or any proper combination thereof.

In some embodiments, the client and server may communicate using anycurrently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form ormedium of digital data communication (e.g., a communication network).Examples of the communication network include local area networks(“LANs”), wide area networks (“WANs”), the Internet (e.g., theInternet), and end-to-end networks (e.g., ad hoc end-to-end networks),as well as any currently known or future developed networks.

The computer-readable medium may be included in the electronic device;or may exist alone without being assembled into the electronic device.

The computer-readable medium carries one or more programs. The one ormore programs, when executed by the electronic device, cause theelectronic device: to acquire a recognition character string matchingwith the speech information; acquire, for each recognition character inthe recognition character string, at least one entity vocabularycorresponding to the recognition character in an entity vocabularylibrary; determine, based on a hit of the entity vocabulary to therecognition character string, a matched entity vocabulary as a semanticdetermining result of the speech information.

The computer program code for performing the operations disclosed in theembodiments of the present disclosure may be written in one or moreprogramming languages or combinations thereof. The programming languagesinclude but not limited to, an object-oriented programming language,such as Java, Smalltalk, and C++, and a conventional proceduralprogramming language, such as C language or a similar programminglanguage. The program code may be executed entirely on a user computer,partially on the user computer, as a standalone software package,partially on the user computer and partially on a remote computer, orentirely on the remote computer or a server. In a case involving aremote computer, the remote computer may be connected to a user computeror an external computer through any kind of network including local areanetwork (LAN) or wide area network (WAN). For example, the remotecomputer may be connected through Internet connection by an Internetservice provider.

Flow charts and block diagrams in the drawings illustrate thearchitecture, functions and operations that can be implemented by thesystem, method and computer program product according to the embodimentsof the present disclosure. Each block in a flowchart or a block diagrammay represent a module, a program segment, or a part of code, and partof the module, program segment, or part of code contains one or moreexecutable instructions for implementing the specified logical function.It should be noted that, in some alternative implementations, thefunctions marked in blocks may be performed in an order different fromthe order shown in the drawings. For example, two blocks shown insuccession may actually be executed in parallel, or sometimes may beexecuted in a reverse order, which depends on the functions involved. Itshould also be noted that each of the block in the block diagram and/orflowchart and a combination of the blocks in the block diagram and/orflowchart may be implemented by a dedicated hardware-based system thatperforms specified functions or actions, or may be realized by acombination of dedicated hardware and computer instructions.

The units mentioned in the description of the embodiments of the presentdisclosure may be implemented by means of software, or otherwise bymeans of hardware. The name of the unit does not constitute a limitationof the unit itself in some cases. For example, the recording executionmodule may be described as “configured to record the position of thesliding starting point and the size of the sliding window”. Thefunctions described above herein may be performed at least in part byone or more hardware logic components. For example, For example, withoutlimitation, exemplary types of hardware logic components that may beused include: Field Programmable Gate Array (FPGA), Application SpecificIntegrated Circuit (ASIC), Application Specific Standard Product (ASSP),System on Chip (SOC), Complex Programmable Logical device (CPLD) and thelike.

In the present disclosure, a machine readable medium may be a tangiblemedium, which may contain or store a program used by the instructionexecution system, apparatus, or device or a program used in combinationwith the instruction execution system, apparatus, or device. The machinereadable medium may be a machine readable signal medium or a machinereadable storage medium. The machine readable medium may include, but isnot limited to, system, an apparatus, or a device in an electronic,magnetic, optical, electromagnetic, infrared, or semi-conductive form,or any suitable combination thereof. More specific examples of themachine readable storage medium may include, one or more wire basedelectrical connections, a portable computer disk, a hard disk, a randomaccess memory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Fast flash memory), an optical fiber, aportable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device or any proper suitable combinationthereof.

According to one or more embodiments of the present disclosure, asemantic determining method is provided in a first example. The semanticdetermining method includes:

acquiring a recognition character string matching with speechinformation;

acquiring, for each recognition character in the recognition characterstring, at least one entity vocabulary corresponding to the recognitioncharacter in an entity vocabulary library; and

determining, based on a hit of the entity vocabulary to the recognitioncharacter string, a matched entity vocabulary as a semantic determiningresult of the speech information.

According to one or more embodiments of the present disclosure, themethod of the first example is provided in a second example. The methodfurther includes:

establishing a descending index table matching with the entityvocabulary library, based on entity vocabularies in the entityvocabulary library, where the descending index table includes aplurality of key-value pairs, each of the key-value pairs includes a keyname which is a character, and a value which is at least one entityvocabulary containing the character; and

querying the descending index table matching with the entity vocabularylibrary, and acquiring at least one entity vocabulary corresponding tothe recognition character in the recognition character string.

According to one or more embodiments of the present disclosure, themethod of the first example is provided in a third example. The methodfurther includes:

traversing, based on a character length of each of the entityvocabularies, local recognition character strings matching with thecharacter length in the recognition character string; and calculatinglocal similarities between the local recognition character strings andthe matched entity vocabulary; and

determining the entity vocabulary matching with the speech information,based on the local similarities between the recognition character stringand each of the entity vocabularies.

According to one or more embodiments of the present disclosure, themethod of the third example is provided in a fourth example. The methodfurther includes:

acquiring a target character length matching with a target entityvocabulary currently processed, and setting a sliding window matchingwith the target character length;

acquiring, based on a position of a sliding starting point of thesliding window in the recognition character string, a target localrecognition character string matching with the sliding window, whereinan initial position of the sliding starting point is a first characterof the recognition character string;

calculating a similarity between the target entity vocabulary and thetarget local recognition character string as a local similarity; and

updating the position of the sliding starting point to a position of anext character, returning to an operation of acquiring the target localrecognition character string matching with the sliding window based onthe position of the sliding starting point of the sliding window in therecognition character string, until the target local recognitioncharacter string includes a last character of the recognition characterstring.

According to one or more embodiments of the present disclosure, themethod of the fourth example is provided in a fifth example. The methodfurther includes:

selecting a maximum local similarity from the local similarities betweenthe recognition character string and each of the entity vocabularies, asa character similarity between the recognition character string and eachof the entity vocabularies; and

determining, based on the character similarity and a preset charactersimilarity threshold condition, the entity vocabulary matching with thespeech information.

According to one or more embodiments of the present disclosure, themethod of the fifth example is provided in a sixth example. The methodfurther includes:

recording the position of the sliding starting point and a size of thesliding window;

acquiring, based on the target character similarity corresponding to thematched entity vocabulary, the position of the sliding starting pointand the size of the sliding window corresponding to the target charactersimilarity; and

adding the acquired position of the sliding starting point and theacquired size of the sliding window to the semantic determining resultas remark information.

According to one or more embodiments of the present disclosure, themethod of the first example is provided in a seventh example. The methodfurther includes:

acquiring valid recognition characters in the recognition characterstring, based on the recognition character string and a preset invalidcharacter list; and

acquiring, for each of the effective recognition characters in therecognition character string, at least one entity vocabularycorresponding to the effective recognition character in the entityvocabulary library.

According to one or more embodiments of the present disclosure, asemantic determining apparatus is provided in an eighth example. Thesemantic determining apparatus further includes:

a recognition character string acquisition module configured to acquirea recognition character string matching with speech information;

an entity vocabulary acquisition module configured to acquire, for eachrecognition character in the recognition character string, at least oneentity vocabulary corresponding to the recognition character in anentity vocabulary library; and

a semantic determining result determination module configured todetermine a matched entity vocabulary as a semantic determining resultof the speech information, based on a hit of each entity vocabulary tothe recognition character string.

According to one or more embodiments of the present disclosure, anelectronic device is provided in a ninth example. The electronic deviceincludes: a memory, a processing device, and a computer program storedon the memory and executable on the processing device, wherein theprocessing device, when executing the program, implements the semanticdetermining method according to any one of the first to seventhexamples.

According to one or more embodiments of the present disclosure, astorage medium containing computer executable instructions is providedin a tenth example, where the computer executable instructions, whenexecuted by a computer processor, implements the semantic determiningmethod according to any one of the first to seventh examples.

The above descriptions are only preferred embodiments of the presentdisclosure and explanations of the technical principles used in thepresent disclosure. Those skilled in the art should understand that thescope of the present disclosure is not limited to the technical solutionformed by combination of the technical features described above, butalso covers other technical solutions formed by any combination of theabove technical features or the equivalent features of the technicalfeatures without departing from the concept of the present disclosure.For example, a technical solution formed by interchanging the abovefeatures and technical features having similar functions as disclosed,but not limited to, in the present disclosure with each other is alsocovered with the scope of the present disclosure.

In addition, although the above operations are described in a specificorder, it should not be understood that these operations are required tobe performed in the specific order or performed in a sequential order.In some conditions, multitasking and parallel processing may beadvantageous. Although multiple implementation details are included inthe above descriptions, the details should not be interpreted aslimitations to the scope of the present disclosure. Some featuresdescribed in an embodiment may be implemented in combination in anotherembodiment. In addition, the features described in an embodiment may beimplemented individually or in any suitable sub-combination form inmultiple embodiments.

Although the subject of the present disclosure has been describedaccording to the structural features and/or logical actions of themethod, it should be understood that the subject defined in the claimsis not necessarily limited to the features or actions described above.The specific features and actions described above are only examples ofthe implementation of the claims.

1. A semantic determining method, comprising: acquiring a recognitioncharacter string matching with speech information; acquiring, for eachrecognition character in the recognition character string, at least oneentity vocabulary corresponding to the recognition character in anentity vocabulary library; and determining, based on a hit of eachentity vocabulary to the recognition character string, a matched entityvocabulary as a semantic determining result of the speech information.2. The method according to claim 1, wherein before acquiring therecognition character string matching with the speech information, themethod further comprises: establishing a descending index table matchingwith the entity vocabulary library, based on entity vocabularies in theentity vocabulary library, wherein the descending index table includes aplurality of key-value pairs, each of the key-value pairs includes a keyname which is a character, and a value which is at least one entityvocabulary containing the character; and the acquiring, for eachrecognition character in the recognition character string, at least oneentity vocabulary corresponding to the recognition character in anentity vocabulary library comprising: querying the descending indextable matching with the entity vocabulary library, and acquiring the atleast one entity vocabulary corresponding to the recognition characterin the recognition character string.
 3. The method according to claim 1,wherein in a case that the entity vocabularies and the recognitioncharacter string have the same number of characters, the determining,based on a hit of each entity vocabulary to the recognition characterstring, a matched entity vocabulary as a semantic determining result ofthe speech information comprises: calculating, for each entityvocabulary, a similarity between the entity vocabulary and therecognition character string, based on the number of the same charactersbetween the entity vocabulary and the recognition character string; anddetermining, based on the similarity between the entity vocabulary andthe recognition character string, the entity vocabulary matching withthe speech information.
 4. The method according to claim 1, wherein in acase that the entity vocabularies and the recognition character stringhave different numbers of characters, the determining, based on a hit ofeach entity vocabulary to the recognition character string, a matchedentity vocabulary as a semantic determining result of the speechinformation comprises: traversing, based on a character length of eachof the entity vocabularies, local recognition character strings matchingwith the character length in the recognition character string; andcalculating local similarities between the local recognition characterstrings and the matched entity vocabulary; and determining the entityvocabulary matching with the speech information, based on the localsimilarities between the recognition character string and each of theentity vocabularies.
 5. The method according to claim 4, wherein thetraversing, based on a character length of each of the entityvocabularies, local recognition character strings matching with thecharacter length in the recognition character string, and calculatinglocal similarities between the local recognition character strings andthe matched entity vocabulary comprises: acquiring a target characterlength matching with a target entity vocabulary currently processed, andsetting a sliding window matching with the target character length;acquiring, based on a position of a sliding starting point of thesliding window in the recognition character string, a target localrecognition character string matching with the sliding window, whereinan initial position of the sliding starting point is a first characterof the recognition character string; calculating a similarity betweenthe target entity vocabulary and the target local recognition characterstring as a local similarity; and updating the position of the slidingstarting point to a position of a next character, returning to anoperation of acquiring the target local recognition character stringmatching with the sliding window based on the position of the slidingstarting point of the sliding window in the recognition characterstring, until the target local recognition character string includes alast character of the recognition character string.
 6. The methodaccording to claim 5, wherein the determining the entity vocabularymatching with the speech information, based on the local similaritiesbetween the recognition character string and each of the entityvocabularies comprises: selecting a maximum local similarity from thelocal similarities between the recognition character string and each ofthe entity vocabularies, as a character similarity between therecognition character string and each of the entity vocabularies; anddetermining, based on the character similarity and a preset charactersimilarity threshold condition, the entity vocabulary matching with thespeech information.
 7. The method according to claim 6, wherein thepreset character similarity threshold condition comprises acorrespondence between a character length of the entity vocabulary and acharacter similarity threshold.
 8. The method according to claim 6,wherein after calculating a similarity between the target entityvocabulary and the target local recognition character string as a localsimilarity, the method further comprises: recording the position of thesliding starting point and a size of the sliding window; and thedetermining, based on a hit of each entity vocabulary to the recognitioncharacter string, a matched entity vocabulary as a semantic determiningresult of the speech information further comprises: acquiring, based ona target character similarity corresponding to the matched entityvocabulary, the position of the sliding starting point and the size ofthe sliding window corresponding to the target character similarity; andadding the acquired position of the sliding starting point and theacquired size of the sliding window to the semantic determining resultas remark information.
 9. The method according to claim 1, whereinbefore acquiring, for each recognition character in the recognitioncharacter string, at least one entity vocabulary corresponding to therecognition character in the entity vocabulary library, the methodfurther comprises: acquiring valid recognition characters in therecognition character string based on the recognition character stringand a preset invalid character list; and the acquiring, for eachrecognition character in the recognition character string, at least oneentity vocabulary corresponding to the recognition character in theentity vocabulary library comprises: acquiring, for each of theeffective recognition characters in the recognition character string, atleast one entity vocabulary corresponding to the effective recognitioncharacter in the entity vocabulary library.
 10. An electronic device,comprising: a memory, a processing device; and a stored on the memoryand executable on the processing device, wherein the computer program,when executed by the processing device, causes the processing device to:acquire a recognition character string matching with speech information;acquire, for each recognition character in the recognition characterstring, at least one entity vocabulary corresponding to the recognitioncharacter in an entity vocabulary library; and determine, based on a hitof each entity vocabulary to the recognition character string, a matchedentity vocabulary as a semantic determining result of the speechinformation.
 11. The electronic device according to claim 10, whereinthe computer program, when executed by the processing device, causes theprocessing device to: establish a descending index table matching withthe entity vocabulary library, based on entity vocabularies in theentity vocabulary library, wherein the descending index table includes aplurality of key-value pairs, each of the key-value pairs includes a keyname which is a character, and a value which is at least one entityvocabulary containing the character; and query the descending indextable matching with the entity vocabulary library, and acquire the atleast one entity vocabulary corresponding to the recognition characterin the recognition character string.
 12. The electronic device accordingto claim 10, wherein in a case that the entity vocabularies and therecognition character string have the same number of characters, thecomputer program, when executed by the processing device, causes theprocessing device to: calculate, for each entity vocabulary, asimilarity between the entity vocabulary and the recognition characterstring, based on the number of the same characters between the entityvocabulary and the recognition character string; and determine, based onthe similarity between the entity vocabulary and the recognitioncharacter string, the entity vocabulary matching with the speechinformation.
 13. The electronic device according to claim 10, wherein ina case that the entity vocabularies and the recognition character stringhave different numbers of characters, the the computer program, whenexecuted by the processing device, causes the processing device to:traverse, based on a character length of each of the entityvocabularies, local recognition character strings matching with thecharacter length in the recognition character string; and calculatelocal similarities between the local recognition character strings andthe matched entity vocabulary; and determine the entity vocabularymatching with the speech information, based on the local similaritiesbetween the recognition character string and each of the entityvocabularies.
 14. The electronic device according to claim 13, whereinthe computer program, when executed by the processing device, causes theprocessing device to: acquire a target character length matching with atarget entity vocabulary currently processed, and set a sliding windowmatching with the target character length; acquire, based on a positionof a sliding starting point of the sliding window in the recognitioncharacter string, a target local recognition character string matchingwith the sliding window, wherein an initial position of the slidingstarting point is a first character of the recognition character string;calculate a similarity between the target entity vocabulary and thetarget local recognition character string as a local similarity; andupdate the position of the sliding starting point to a position of anext character, return to an operation of acquiring the target localrecognition character string matching with the sliding window based onthe position of the sliding starting point of the sliding window in therecognition character string, until the target local recognitioncharacter string includes a last character of the recognition characterstring.
 15. The electronic device according to claim 14, wherein thecomputer program, when executed by the processing device, causes theprocessing device to: select a maximum local similarity from the localsimilarities between the recognition character string and each of theentity vocabularies, as a character similarity between the recognitioncharacter string and each of the entity vocabularies; and determine,based on the character similarity and a preset character similaritythreshold condition, the entity vocabulary matching with the speechinformation.
 16. The electronic device according to claim 15, whereinthe preset character similarity threshold condition comprises acorrespondence between a character length of the entity vocabulary and acharacter similarity threshold.
 17. The electronic device according toclaim 15, wherein the computer program, when executed by the processingdevice, causes the processing device to: record the position of thesliding starting point and a size of the sliding window; acquire, basedon a target character similarity corresponding to the matched entityvocabulary, the position of the sliding starting point and the size ofthe sliding window corresponding to the target character similarity; andadd the acquired position of the sliding starting point and the acquiredsize of the sliding window to the semantic determining result as remarkinformation.
 18. The electronic device according to claim 10, whereinthe computer program, when executed by the processing device, causes theprocessing device to: acquire valid recognition characters in therecognition character string based on the recognition character stringand a preset invalid character list; and acquire, for each of theeffective recognition characters in the recognition character string, atleast one entity vocabulary corresponding to the effective recognitioncharacter in the entity vocabulary library.
 19. A non-transitory storagemedium containing computer executable instructions, wherein theinstructions, when executed by a computer processor, cause the computerprocessor to acquire a recognition character string matching with speechinformation; acquire, for each recognition character in the recognitioncharacter string, at least one entity vocabulary corresponding to therecognition character in an entity vocabulary library; and determine,based on a hit of each entity vocabulary to the recognition characterstring, a matched entity vocabulary as a semantic determining result ofthe speech information.
 20. The non-transitory storage medium accordingto claim 19, wherein the instructions, when executed by a computerprocessor, cause the computer processor to: establish a descending indextable matching with the entity vocabulary library, based on entityvocabularies in the entity vocabulary library, wherein the descendingindex table includes a plurality of key-value pairs, each of thekey-value pairs includes a key name which is a character, and a valuewhich is at least one entity vocabulary containing the character; andquery the descending index table matching with the entity vocabularylibrary, and acquire the at least one entity vocabulary corresponding tothe recognition character in the recognition character string.